The 2017 DAVIS Challenge on Video Object Segmentation

TL;DR

The 2017 DAVIS Challenge on Video Object Segmentation introduced a new dataset, benchmark, and competition to advance research in video object segmentation, fostering the development of state-of-the-art techniques.

Contribution

This paper presents the scope, dataset, evaluation metrics, and results analysis of the 2017 DAVIS Challenge, promoting progress in video object segmentation research.

Findings

Benchmark established for video object segmentation.

Participants achieved improved segmentation accuracy.

The dataset facilitated the development of new algorithms.

Abstract

We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation. Following the footsteps of other successful initiatives, such as ILSVRC and PASCAL VOC, which established the avenue of research in the fields of scene classification and semantic segmentation, the DAVIS Challenge comprises a dataset, an evaluation methodology, and a public competition with a dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which has fostered the development of several novel state-of-the-art video object segmentation techniques. In this paper we describe the scope of the benchmark, highlight the main characteristics of the dataset, define the evaluation metrics of the competition, and present a detailed analysis of the results of the participants to the challenge.

Full text

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